基于高分遥感影像的建筑物侧面信息提取及其高度计算

本文选取成都市某一区域建筑物A、B为研究对象,采用分辨率为0.61 m的Quick bird影像,运用图像分割法和LVQ神经网络算法,提取建筑物侧面信息,根据假设法原理,构建高度计算物理模型,求取建筑物高度。对比实测数据,结合可能影响实验结果的实地因素、遥感影像因素进行精度分析与评价,探讨基于高分遥感影像的建筑物侧面信息提取和高度计算的方法。结果表明,LVQ神经网络算法在建筑物侧面提取和高度计算中有更好的应用价值,精度高达94%。     

ASTER GDEM V2的南极冰川高程误差校正及精度分析

ASTER GDEM V2是研究南极冰盖表面的一种重要DEM数据源。由于南极冰雪区反射率高且缺乏地形特征,导致ASTER GDEM V2存在大量的坑、隆起等噪声,难以直接用于南极地形分析。本文以ICESat/GLAS激光点高程数据作为参考,采用修正等高线法对南极伯德（Byrd）冰川ASTER GDEM V2进行了误差校正,并将其与ICESat-1 DEM的垂直精度进行了对比分析。结果表明：ASTER GDEM V2的RMSE由校正前的26.56 m下降到校正后的18.77 m,远低于ICESat-1 DEM的RMSE（121.24 m）;校正后的ASTER GDEM V2高程精度受坡度影响较小,不存在明显的系统误差,而ICESat-1 DEM的高程精度受坡度的影响较大。本研究进一步通过地形剖面分析得到：校正前的ASTER GDEM V2噪声主要分布于高程较低、地形平坦的区域,通过修正等高线的方法可以有效去除这些噪声,去除噪声后的ASTER GDEM V2可作为研究伯德冰川理想的DEM数据源。     

Mapping 3D visibility in an urban street environment from mobile LiDAR point clouds

ABSTRACT

Visibility determination is a key requirement in a wide range of national and urban applications, such as national security, landscape management, and urban design. Mobile LiDAR point clouds can depict the urban built environment with a high level of details and accuracy. However, few three-dimensional visibility approaches have been developed for the street-level point-cloud data. Accordingly, an approach based on mobile LiDAR point clouds has been developed to map the three-dimensional visibility at the street level. The method consists of five steps: voxelization of point-cloud data, construction of lines-of-sight, construction of sectors of sight, construction of three-dimensional visible space, and calculation of volume index. The proposed approach is able to automatically measure the volume of visible space and openness at any viewpoint along a street. This approach has been applied to three study areas. The results indicated that the proposed approach enables accurate simulation of visible space as well as high-resolution (1 m × 1 m) mapping of the visible volume index. The proposed approach can make a contribution to the improvement of urban planning and design processes that aim at developing more sustainable built environments.     

A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

ABSTRACT

Earth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.     

Three new species of Anthomedusae (Hydrozoa: Hydroidomedusa) from the Guangdong coastal water, China

The samples of Anthomedusae were collected from the Guangdong coastal water,China.Three new species of Anthomedusae,i.e.Zhangiella condensum Huang,Zhang et Sun,sp.nov.,Hydractinia leizhouensis Huang,Zhang et Yang,sp.nov.,and Cladosarsia simplex Huang,Zhang et Ke,sp.nov.are described.All type specimens are deposited in College of Ocean and Earth Sciences,Xiamen University.     

2012-2016年调水调沙对黄河口海域大型底栖动物群落结构的影响

为了明确黄河调水调沙对河口海域大型底栖动物群落结构的影响,本研究于2012至2016年调水调沙后进行了大型底栖动物样品的采集工作。整个研究时期共鉴定出大型底栖动物7门138种,生物量和丰度2012年显著高于2016年,生物量年间无显著差异;研究时期内优势种以小型多毛类为主,鲜见软体动物、节肢动物和棘皮动物,其中2016年优势种全部为小个体多毛类;MDS显示,随时间的推移距河口不同距离梯度的大型底栖动物逐渐演变为相同的群落结构,且Shannon-Wiener多样性指数和Margalef丰富度指数呈下降趋势;CCA显示研究海域主要底栖动物种类的生态需求为高中值粒径、低溶解氧、pH、黏土和硫化物含量。本研究推测调水调沙工程一定程度上影响了底层生物群落结构,特别是研究海域的A断面。     

Parametric Vibration Analysis of Submerged Floating Tunnel Tension Legs

According to the characteristics of submerged floating tunnel anchored by tension legs,simplifying the tube as point mass and assuming that the tension leg is a nonlinear beam model hinged at both ends,the nonlinear vibration equation of the tension leg is derived.The equation is solved by the Galerkin method and Runge Kutta method.Subsequently,numerical analysis of typical submerged floating tunnel tension leg is carried out.It is shown that,the parametric vibration response of the submerged floating tunnel tension leg is related to the amplitude and frequency of the end excitation.Without considering axial resonance and transverse resonance,it is reasonable that higher order modes are abandoned and only the first three modes are considered.The axial resonance amplitude of the second or third order mode is equivalent to the first order mode axial resonance amplitude,which should not be ignored.     

基于大数据分析下的气候模型

为研究全球变暖与极寒天气间的关系,对加拿大13个省代表性测站10年的观测数据进行时空变化趋势分析,采用经验正交函数（EOF）寻找海洋表面温度历史数据的变化规律。另外利用BP神经网络建立了年平均温度、日降水量与地球吸热、散热、海表面温度、当地纬度间的关系,预测未来25年气候的变化,并建立了“极寒天气”与气候变化的关系模型。研究表明：高纬度地区温度、降水量普遍较低,同经度地区的温度差异较小且降水量变化不大;加拿大地区温度呈周期性变化,符合北半球的季节变化特征;北大西洋的东部与其他海洋的温度是反相关的,西太平洋南北回归线附近的海洋表面温度升高;“极寒天气”出现频率与气候变化有一定关系,局地极寒现象与全球变暖的大趋势并不矛盾。本研究为人们认识和理解“全球变暖”提供了一个新的思路。     

海洋脊椎动物甲状腺系统的研究与应用

甲状腺（Glandula thyreoidea）是海洋脊椎动物体内重要的内分泌腺,从海洋脊椎动物开始,才有了独立的甲状腺结构。海洋脊椎动物的甲状腺一般为实质性的组织。实质由甲状腺滤泡组成,构成滤泡的上皮细胞合成、贮存和分泌的甲状腺激素是调节机体生长发育的关键激素。从海洋脊椎动物开始才有了独立的甲状腺结构。对于动物甲状腺的研究多集中在甲状腺形态及甲状腺与生长发育关系方面,而对海洋脊椎动物的甲状腺的研究则越来越多的集中于环境因素所引起的甲状腺病变方面,环境因素对甲状腺功能的影响也逐渐成为科学研究的重点。海洋水体污染导致的甲状腺机能受损,使海洋脊椎动物不能正常生长发育,数目与种类骤减之时,我们不得不重视对海洋脊椎动物甲状腺的研究。     

天津沿海赤潮发生的基本特征研究

使用《天津市海洋环境状况公报》中的数据,分析天津沿海赤潮发生的特征。通过分析发现2005-2017年天津海域共发生赤潮30次,累计发生面积4 695 km2。天津沿海赤潮发生主要集中在夏季,又以8月最多,占赤潮发生总数的30%。通过赤潮优势种的分析发现近年来天津海域赤潮生物的优势种类有不断增加的趋势。同时本文从水文气象条件诱发赤潮的角度分析得出了天津沿海最易发生赤潮的天气形势,天津沿海25~27℃水温范围内赤潮发生较多,最适宜赤潮发生的风速为3、4级风,风向为东南风、西南风。